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Using Differential Evolution to Improve Pheromone-Based Coordination of Swarms of Drones for Collaborative Target Detection

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Using Differential Evolution to Improve Pheromone-Based Coordination of Swarms of Drones for Collaborative Target Detection Using Differential Evolution to Improve Pheromone-based Coordination of Swarms of Drones for Collaborative Target Detection Mario G. C. A. Cimino, Alessandro Lazzeri and Gigliola Vaglini Department of Information Engineering, Università di Pisa, Largo Lazzarino 1, Pisa, Italy Keywords: Differential Evolution, Parametric Adaptation, Collaborative Target Detection, Marker-based Stigmergy, Swarm Intelligence. Abstract: In this paper we propose a novel algorithm for adaptive coordination of drones, which performs collaborative target detection in unstructured environments. Coordination is based on digital pheromones released by drones when detecting targets, and maintained in a virtual environment. Adaptation is based on the Differential Evolution (DE) and involves the parametric behaviour of both drones and environment. More precisely, attractive/repulsive pheromones allow indirect communication between drones in a flock, concerning the availability/unavailability of recently found targets. The algorithm is effective if structural parameters are properly tuned. For this purpose DE combines different parametric solutions to increase the swarm performance. We focus first on the study of the principal parameters of the DE, i.e., the crossover rate and the differential weight. Then, we compare the performance of our algorithm with three different strategies on six simulated scenarios. Experimental results show the effectiveness of the approach. 1 INTRODUCTION AND In our approach, stigmergy and flocking are exploited to enable self-coordination in the MOTIVATION navigation of unstructured environments. Such environments typically contain a number of The Differential Evolution algorithm (DE) is a obstacles such as trees and buildings. Targets are stochastic population based algorithm, very suited placed according to different patterns. More for numerical and multi-modal optimization specifically, stigmergy implies the use of an problems. Recently, DE has been applied in many attractive digital pheromone to locally coordinate research and application areas. Compared with other drones. When a drone detects a new piece of target, population-based algorithms, such as genetic it releases the attractive pheromone. Other drones in algorithm and particle swarm optimization, DE the neighbourhood can sense and follow the exhibits excellent performance both in unimodal, pheromone gradient to cooperate in detecting the multimodal, separable, and non-separable problems. pattern of targets. With respect to (Cimino, 2015b) Moreover, it is much simpler to use because it has in this study we sensibly improve the performance only three parameters (Das, 2011): the population N, of the algorithm, by adopting the parametric DE- the differential weight F, and the crossover rate CR. based adaptation, by introducing repulsive When managing the DE algorithm, the main effort is pheromone and scattering mechanisms. Basically, to properly set the parameters in order to avoid flocking implies a set of three behavioural rules, premature convergence towards a non-optimal named separate, align, and scatter. It maintains the solution. As reported by literature, there is no drones in groups enhancing the stigmergy when dominant setting, and for each problem a proper occurring. In contrast, the repulsive pheromone study of the behaviour of DE is needed to find the helps the drones to avoid multiple exploration of the correct parameterization. same zone. Indeed, the drone releases a repulsive In this paper, we adopt DE algorithm to optimize pheromone whereas it does not sense a target. the behaviour of a swarm of aerial drones carrying Finally, the scattering rule makes a drone to turn out collaborative target detection (Cimino, 2015b). 605 Cimino, M., Lazzeri, A. and Vaglini, G. Using Differential Evolution to Improve Pheromone-based Coordination of Swarms of Drones for Collaborative Target Detection. DOI: 10.5220/0005732606050610 In Proceedings of the 5th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2016), pages 605-610 ISBN: 978-989-758-173-1 Copyright c 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved ICPRAM 2016 - International Conference on Pattern Recognition Applications and Methods by an angle when it tails another drone. position of each robot, the next position, and the In the literature there are two design methods to possible collision are provided to DE. In the develop collective behaviours in swarm systems: decentralized formulation, each robot runs DE for behaviour-based design and automatic design itself considering the information of neighbour (Brambilla, 2013). The former implies the robots. Authors conclude that the decentralized developers to implement, study, and improve the approach needs less time in comparison to the behaviour of each single individual until the desired centralized one; moreover the performance is collective behaviour is achieved. This is the comparable to PSO. In our approach, we consider to approach adopted in (Cimino, 2015b). The latter is use DE offline to find a proper and general purpose usually used to reduce the effort of the developers. parameter tuning for the swarms. Moreover, in our Automatic design methods can be furtherly divided formulation drones have a limited computing in two categories: reinforcement learning and capability, and then an online execution of DE is not evolutionary robotics (Brambilla, 2013). The first feasible. implies a definition at the individual level of positive In (Cruz-Alvarez, 2013) DE/1/rand/bin is used and repulsive reinforce to give reward to the with F=0.7, CR=1.0, N=120 for 250 generations, and individual. In general, it is usually hard for the another variant called DE/1/best/bin is used with developer to decompose the collective output of the F=0.8, CR=1.0, N=150 for 200 generations to tune swarm in individual rewards. Evolutionary robotics the behaviour of a robot in wall-following task. Here implies evolutionary techniques inspired by the it seems that DE/1/best/bin is able to find a slightly Darwinian principle of selection and evolution. better solution than DE/1/rand/bin. However, Generally in these methods each swarm consists of authors used different parameters settings (F, N and individuals with the same behaviour. A population number of generations) for each variant, thus a of swarms is then computed, where each population comparative analysis is difficult. In our approach we member has a particular behaviour. A simulation is focus on DE/1/rand/bin variant and evaluate several made for each member and a fitness function is combinations of CR and F. computed. Then through a mutation and crossover procedure a new generation is computed. This process iteratively repeats improving the 3 SWARM BEHAVIORAL MODEL performance of the swarm population. In this section we improve the swarm algorithm of (Cimino, 2015b). We refer to the time unit as a tick, 2 RELATED WORK i.e., an update cycle of both the environment and the drones. Each drone is equipped with: (a) wireless DE has been used in several domains for communication device for sending and receiving optimization and parameterization tasks (Das, 2011). information from a ground station; (b) self-location As an example, in (Nikolos, 2005) the authors used a capability, e.g. based on global position system classical DE variant, namely DE/1/rand/bin, to (GPS); (c) a sensor to detect a target in proximity of coordinate multiple drones navigating from a known the drone; (d) processor with limited computing initial position to a predetermined target location. capability; (e) a sensor to detect obstacles. Here, DE is set up with N=50, F=1.05 and CR=0.85. The environment and the pheromone dynamics The algorithm was defined to terminate in 200 We consider a predefined area that contains a set generations, but it usually converges in 30 iterations. of targets to be identified. The environment is Our problem sensibly differs, because the target modelled by a digital grid corresponding to the position is unknown, and our approach is physical area. The grid has C2 cells, each identified independent of the initial position. by (x, y) coordinates with x, y ∈ {1,…,C}. The In (Chakraborty, 2008) the authors confront DE actual size of the area and the granulation of the grid and Particle Swarm Optimization (PSO) for co- depend on the domain application. Figure 1 shows operative distributed multi-robot path planning Pheromone dynamics in an urban scenario. Here, the problem. As for (Nikolos and Brintaki, 2005) initial intensity of the pheromone is represented as a dark position of the robots and final position are known. colour, and each target is represented by an “X”. A Here, both centralized and decentralized darker gradation means higher pheromone intensity. formulations are proposed. In the centralized At the beginning, the pheromone is in one cell at its approach, DE minimizes the distance for the next maximum intensity, and then it diffuses to step of each robot. In this case all information of the 606 Using Differential Evolution to Improve Pheromone-based Coordination of Swarms of Drones for Collaborative Target Detection neighbouring cells. After a certain time the Every tick period, represented by the hourglass, pheromone evaporates, disappearing from the the drone performs two parallel processes: (a) the environment. When a drone detects a piece of the target detection, which corresponds to the release of overall target, a pheromone of intensity I is released
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